As is known in the art, many techniques used in the quantitative analysis of objects, such as anatomies and pathologies, from large three-dimensional (3D) volume of imaging data, such as CT data, includes the segmentation of the objects from neighboring-objects. Over the last decade, shape-based segmentation methods have become more and more common. First introduced in 1995, active shape models (ASM) and active appearance models (AAM), as described by T. F. Cootes and C. J. Taylor in “Statistical models of appearance for computer vision”, Technical Report, University of Manchester, 2004 have been very popular tools for the segmentation of anatomical structures in medical images. See also: J. G. Bosch, S. C. Mitchell, B. P. F. Lelieveldt, F. Nikland, O. Kamp, M. Sonka, and J. H. Reiber “Automatic segmentation of echocardiographic sequences by active appearance motion models”, IEEE Trans. Medical Imaging, 21(1 1):1374-1383, 2002; Duta and M. Sonka “Segmentation and interpretation of MR brain images: An improved active shape model”, IEEE Trans. Medical Imaging, 17(6):1049-1062, 1998; and A. Lundervold, N. Duta, T. Taxt, and A. Jain. “Model-guided segmentation of corpus callosum in MR images” CVPR, pages 123 1-1238, 1999.
More recently, principal component analysis (PCA) has also been applied to distance transforms for an implicit representation of shapes, see M. Leventon, E. Grimson, and O. Faugeras, “Statistical Shape Influence in Geodesic Active Contours” IEEE Conference on Computer Vision and Pattern Recognition, pages I:3 16-322, 2000. Shape-based segmentation is usually equivalent to recovering a geometric structure which is both highly probable in the model space and well aligned with strong features in the image. The advantage of the shape based methods over classical deformable templates is that they allow the deformation process to be constrained to remain within the space of allowable shapes, see T. McInerney, G. Hamarneh, M. Shenton, and D. Terzopoulos. Deformable organisms for automatic medical image analysis. Medical Image Analysis, 6:251-266, 2002. These methods have proven to be a good compromise between complexity and shape generalization. However, since modeling is performed after registration, errors in the registration can be propagated into the model space. Furthermore, the assumption of Gaussian shape models might be a little restrictive.